On the Unbounded External Archive and Population Size in Preference-based Evolutionary Multi-objective Optimization Using a Reference Point
On the Unbounded External Archive and Population Size in Preference-based Evolutionary Multi-objective Optimization Using a Reference Point
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DOI：
10.1145/3583131.3590511
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发表时间：
2023-04
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影响因子：
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通讯作者：
Ryoji Tanabe
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文献类型：
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作者：
Ryoji Tanabe
虽然种群规模是进化多目标优化（EMO）中的一个重要参数，但它对基于偏好的EMO（PBEMO）的影响却知之甚少。PBEMO中无界外部存档（UA）的有效性也知之甚少，其中UA维护迄今为止发现的所有非支配解决方案。此外，现有的方法后处理的UA不能处理决策者的偏好信息。在这种情况下，首先，本文提出了一种基于偏好的后处理方法，用于从UA中选择有代表性的解决方案。然后，我们研究了UA和人口规模对PBEMO算法性能的影响。我们的结果表明，PBEMO算法的性能（例如，R-NSGA-II）可以通过使用UA和所提出的方法显著改善。我们证明了一个较小的人口规模比常用的是有效的，在大多数PBEMO算法的小预算的功能评估，即使是许多目标。我们发现，感兴趣的区域的大小是一个不太重要的因素，在选择人口规模的PBEMO算法在现实世界中的问题。
Although the population size is an important parameter in evolutionary multi-objective optimization (EMO), little is known about its influence on preference-based EMO (PBEMO). The effectiveness of an unbounded external archive (UA) in PBEMO is also poorly understood, where the UA maintains all non-dominated solutions found so far. In addition, existing methods for postprocessing the UA cannot handle the decision maker's preference information. In this context, first, this paper proposes a preference-based postprocessing method for selecting representative solutions from the UA. Then, we investigate the influence of the UA and population size on the performance of PBEMO algorithms. Our results show that the performance of PBEMO algorithms (e.g., R-NSGA-II) can be significantly improved by using the UA and the proposed method. We demonstrate that a smaller population size than commonly used is effective in most PBEMO algorithms for a small budget of function evaluations, even for many objectives. We found that the size of the region of interest is a less important factor in selecting the population size of the PBEMO algorithms on real-world problems.